Long-range Horizontal Connections Research Articles

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations.

When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections.

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex withlayer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex.

Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key components to organize a "small-world network" optimized for each size of the visual cortex, enabling the cost-efficient integration of visual information. Using computational simulations of a biologically inspired model neural network, we found that sparse LRCs added to networks, combined with dense local connections, compose a small-world network and significantly enhance image classification performance. We confirmed that the performance of the network appeared to be strongly correlated with the small-world coefficient of the model network under various conditions. Our theoretical model demonstrates that the amount of LRCs to build a small-world network depends on each size of cortex and that LRCs are beneficial only when the size of the network exceeds a certain threshold. Our model simulation of various sizes of cortices validates this prediction and provides an explanation of the species-specific existence of LRCs in animal data. Our results provide insight into a biological strategy of the brain to balance functional performance and resource cost.

The Generation and Modulation of Distinct Gamma Oscillations with Local, Horizontal, and Feedback Connections in the Primary Visual Cortex: A Model Study on Large-Scale Networks.

Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in information processing. Studies have shown that the response property of GAMMA is related to neural interaction through local recurrent connections (RC), feed-forward (FF), and feedback (FB) connections. However, the relationship between GAMMA and long-range horizontal connections (HC) in the brain remains unclear. Here, we aimed to understand this question in a large-scale network model for the primary visual cortex (V1). We created a computational model composed of multiple excitatory and inhibitory units with biologically plausible connectivity patterns for RC, FF, FB, and HC in V1; then, we quantitated GAMMA in network models at different strength levels of HC and other connection types. Surprisingly, we found that HC and FB, the two types of large-scale connections, play very different roles in generating and modulating GAMMA. While both FB and HC modulate a fast gamma oscillation (around 50-60 Hz) generated by FF and RC, HC generates a new GAMMA oscillating around 30 Hz, whose power and peak frequency can also be modulated by FB. Furthermore, response properties of the two GAMMAs in a network with both HC and FB are different in a way that is highly consistent with a recent experimental finding for distinct GAMMAs in macaque V1. The results suggest that distinct GAMMAs are signatures for neural connections in different spatial scales and they might be related to different functions for information integration. Our study, for the first time, pinpoints the underlying circuits for distinct GAMMAs in a mechanistic model for macaque V1, which might provide a new framework to study multiple gamma oscillations in other cortical regions.

Various effects induced by local synchronization in neural networks

In the cerebral cortex, the large-scale synchronous firing of neurons can cause epilepsy, during which spiral waves can spontaneously occur. The relationship between the synchronous firing of a large number of neurons and the spontaneous generation of spiral waves is still unclear. In this paper, a two-dimensional neuronal network with the long-range coupling regions generated by adding long-range horizontal connections is constructed. The Morris-Lecar neuron model is used to study the propagation of waves in the two-dimensional neuronal network with the rectangular long-range coupling regions. Numerical simulation results show that the plan and target waves whose propagation directions are both parallel to that of the long-range coupling can lead the neurons to be excited synchronously in the long-range coupling region. This synchronous firing is accompanied by delayed firing of some neurons and premature firing of others. When the width of the long-range coupling region exceeds the critical width, all the neurons in the long-range coupling region delay firing. When the width of the long-range coupling region exceeds a maximum conduction width, the waves will not pass through the long-range coupling region. When the size of the rectangular region of the long-range coupling is selected appropriately, the synchronous excitation of neurons can cause network to present the backfiring effect and to have the selectivity of wave propagation direction. Furthermore, the selectivity of wave propagation direction is very sensitive to whether the neuron is in the stationary state and also to the change of coupling strength, so that the high frequency plane wave train can partially pass through the long-range coupling region whose width exceeds the maximum conduction width. So we can control whether the wave can pass through the long-range coupling region of a certain width by giving the neurons in the long-range coupling region a tiny perturbation. When one or two long-range coupling regions are constructed in the neural network and the size of the long-range coupling regions is selected appropriately, the self-sustaining plane wave, spiral wave and target wave can appear spontaneously in the network when the wave passes through the long-range coupling regions. The physical mechanisms behind those phenomena are also analyzed.

A brain-inspired network architecture for cost-efficient object recognition in shallow hierarchical neural networks

The brain successfully performs visual object recognition with a limited number of hierarchical networks that are much shallower than artificial deep neural networks (DNNs) that perform similar tasks. Here, we show that long-range horizontal connections (LRCs), often observed in the visual cortex of mammalian species, enable such a cost-efficient visual object recognition in shallow neural networks. Using simulations of a model hierarchical network with convergent feedforward connections and LRCs, we found that the addition of LRCs to the shallow feedforward network significantly enhances the performance of networks for image classification, to a degree that is comparable to much deeper networks. We found that a combination of sparse LRCs and dense local connections dramatically increases performance per wiring cost. From network pruning with gradient-based optimization, we also confirmed that LRCs could emerge spontaneously by minimizing the total connection length while maintaining performance. Ablation of emerged LRCs led to a significant reduction of classification performance, which implies these LRCs are crucial for performing image classification. Taken together, our findings suggest a brain-inspired strategy for constructing a cost-efficient network architecture to implement parsimonious object recognition under physical constraints such as shallow hierarchical depth.

Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex.

In the primary visual cortex (V1) of higher mammals, long-range horizontal connections (LHCs) are observed to develop, linking iso-orientation domains of cortical tuning. It is unknown how this feature-specific wiring of circuitry develops before eye-opening. Here, we suggest that LHCs in V1 may originate from spatiotemporally structured feedforward activities generated from spontaneous retinal waves. Using model simulations based on the anatomy and observed activity patterns of the retina, we show that waves propagating in retinal mosaics can initialize the wiring of LHCs by coactivating neurons of similar tuning, whereas equivalent random activities cannot induce such organizations. Simulations showed that emerged LHCs can produce the patterned activities observed in V1, matching the topography of the underlying orientation map. The model can also reproduce feature-specific microcircuits in the salt-and-pepper organizations found in rodents. Our results imply that early peripheral activities contribute significantly to cortical development of functional circuits.SIGNIFICANCE STATEMENT Long-range horizontal connections (LHCs) in the primary visual cortex (V1) are observed to emerge before the onset of visual experience, thereby selectively connecting iso-domains of orientation map. However, it is unknown how such feature-specific wirings develop before eye-opening. Here, we show that LHCs in V1 may originate from the feature-specific activation of cortical neurons by spontaneous retinal waves during early developmental stages. Our simulations of a visual cortex model show that feedforward activities from the retina initialize the spatial organization of activity patterns in V1, which induces visual feature-specific wirings in the V1 neurons. Our model also explains the origin of cortical microcircuits observed in rodents, suggesting that the proposed developmental mechanism is universally applicable to circuits of various mammalian species.

Smaller visual arrays are harder to integrate in schizophrenia: Evidence for impaired lateral connections in early vision

Long-range horizontal connections in early vision undergird a well-studied “collinear facilitation” effect, wherein a central low-contrast target becomes more detectable when flanked by collinear elements. Collinear facilitation is weaker in schizophrenia. Might lateral connections be responsible? To consider the possibility, we had 38 schizophrenia patients and 49 well-matched healthy controls judge the presence of a central low-contrast element flanked by collinear or orthogonal high-contrast elements. The display (target+flankers) was scaled in size to produce a lower and higher spatial frequency ("SF") condition (4 and 10 cycles/deg, respectively). Larger stimulus arrays bias processing towards feedback connections from higher-order visual areas; smaller arrays bias processing toward lateral connections. Patients had impaired facilitation relative to controls at higher but not lower SFs. Combining data from a past study on “contour integration” (in which subjects sought to detect chains of co-circular elements), we found correlated integration and facilitation performance at the higher SF and a similar effect of spatial scaling across SF, suggesting a common mechanism. In an exploratory analysis, worse contrast thresholds (without facilitation) correlated strongly with more premorbid dysfunction. In schizophrenia, inter-element filling-in worsens at smaller spatial scales potentially because of its increased reliance on impaired lateral connections in early vision.

From receptive profiles to a metric model of V1.

In this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consistent with the well-known properties of long-range horizontal connections in V1, and it is compatible with the perceptual rules synthesized by the concept of association field. These characteristics are still present when the kernel is constructed from a bank of filters arising from an unsupervised learning algorithm.

Phase Equation for Patterns of Orientation Selectivity in a Neural Field Model of Visual Cortex

In this paper we consider a neural field equation on L${\mathbb R}^2\times \mathbb{S}^1$, which models the activity of populations of spatially organized, orientation selective neurons. In particular, we show how spatially organized patterns of orientation tuning can emerge due to the presence of weak, long-range horizontal connections and how such patterns can be analyzed in terms of a reduced phase equation. The latter is formally identical to the phase equation obtained in the study of weakly coupled oscillators, except that now the spatially distributed phase represents the peak of an orientation tuning curve (stationary pulse or bump on $S^1$) of neural populations at different locations in ${\mathbb R}^2$. We then carry out a detailed analysis of the existence and stability of various solutions to the phase equation and show that the resulting spatially structured phase patterns are consistent with numerical simulations of the full neural field equations. In contrast to previous studies of neural fi...

Very-long-range disynaptic V1 connections through layer 6 pyramidal neurons revealed by transneuronal tracing with rabies virus.

Neurons in primary visual cortex (V1) integrate across the representation of the visual field through networks of long-range projecting pyramidal neurons. These projections, which originate from within V1 and through feedback from higher visual areas, are likely to play a key role in such visual processes as low contrast facilitation and extraclassical surround suppression. The extent of the visual field representation covered by feedback is generally much larger than that covered through monosynaptic horizontal connections within V1, and, although it may be possible that multisynaptic horizontal connections across V1 could also lead to more widespread spatial integration, nothing is known regarding such circuits. In this study, we used injections of the CVS-11 strain of rabies virus to examine disynaptic long-range horizontal connections within macaque monkey V1. Injections were made around the representation of 5° eccentricity in the lower visual field. Along the opercular surface of V1, we found that the majority of connected neurons extended up to 8 mm in most layers, consistent with twice the typically reported distances of monosynaptic connections. In addition, mainly in layer 6, a steady presence of connected neurons within V1 was observed up to 16 mm away. A relatively high percentage of these connected neurons had large-diameter somata characteristic of Meynert cells, which are known to project as far as 8 mm individually. Several neurons, predominantly in layer 6, were also found deep within the calcarine sulcus, reaching as far as 20° of eccentricity, based on estimates, and extending well into the upper visual field representation. Thus, our anatomical results provide evidence for a wide-ranging disynaptic circuit within V1, mediated largely through layer 6, that accounts for integration across a large region of the visual field.

The development of contour processing: evidence from physiology and psychophysics.

Object perception and pattern vision depend fundamentally upon the extraction of contours from the visual environment. In adulthood, contour or edge-level processing is supported by the Gestalt heuristics of proximity, collinearity, and closure. Less is known, however, about the developmental trajectory of contour detection and contour integration. Within the physiology of the visual system, long-range horizontal connections in V1 and V2 are the likely candidates for implementing these heuristics. While post-mortem anatomical studies of human infants suggest that horizontal interconnections reach maturity by the second year of life, psychophysical research with infants and children suggests a considerably more protracted development. In the present review, data from infancy to adulthood will be discussed in order to track the development of contour detection and integration. The goal of this review is thus to integrate the development of contour detection and integration with research regarding the development of underlying neural circuitry. We conclude that the ontogeny of this system is best characterized as a developmentally extended period of associative acquisition whereby horizontal connectivity becomes functional over longer and longer distances, thus becoming able to effectively integrate over greater spans of visual space.

Is 20/20 vision good enough? Visual acuity differences within the normal range predict contour element detection and integration.

Contour integration (CI) combines appropriately aligned and oriented elements into continuous boundaries. Collinear facilitation (CF) occurs when a low-contrast oriented element becomes more visible when flanked by collinear high-contrast elements. Both processes rely at least partly on long-range horizontal connections in early visual cortex, and thus both have been extensively studied to understand visual cortical functioning in aging, development, and clinical disorders. Here, we ask: Can acuity differences within the normal range predict CI or CF? To consider this question, we measured binocular visual acuity and compared subjects with 20/20 vision to those with better-than-20/20 vision (SharpPerceivers) on two tasks. In the CI task, subjects located an integrated shape embedded in varying amounts of noise; in the CF task, subjects detected a low-contrast element flanked by collinear or orthogonal high-contrast elements. In each case, displays were scaled in size to modulate element visibility and spatial frequency (4-12 cycles/deg). SharpPerceivers could integrate contours under noisier conditions than the 20/20 group (p = .0002), especially for high spatial frequency displays. Moreover, although the two groups exhibited similar collinear facilitation, SharpPerceivers could detect the central target with lower contrast at high spatial frequencies (p <. 05). These results suggest that small acuity differences within the normal range--corresponding to about a one line difference on a vision chart--strongly predict element detection and integration. Furthermore, simply ensuring that subjects have normal or corrected-to-normal vision is not sufficient when comparing groups on contour tasks; visual acuity confounds also need to be ruled out.

Large-Scale Axonal Reorganization of Inhibitory Neurons following Retinal Lesions

The functional properties of adult cortical neurons are subject to alterations in sensory experience. Retinal lesions lead to remapping of cortical topography in the region of primary visual cortex representing the lesioned part of the retina, the lesion projection zone (LPZ), with receptive fields shifting to the intact parts of the retina. Neurons within the LPZ receive strengthened input from the surrounding region by growth of the plexus of excitatory long-range horizontal connections. Here, by combining cell type-specific labeling with a genetically engineered recombinant adeno-associated virus and in vivo two-photon microscopy in adult macaques, we showed that the remapping was also associated with alterations in the axonal arbors of inhibitory neurons, which underwent a parallel process of pruning and growth. The axons of inhibitory neurons located within the LPZ extended across the LPZ border, suggesting a mechanism by which new excitatory input arising from the peri-LPZ is balanced by reciprocal inhibition arising from the LPZ.

Network model of top-down influences on local gain and contextual interactions in visual cortex

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent. This suggests an interaction between recurrent inputs to V1 and intrinsic connections within V1 that enables V1 neurons to respond differently under different conditions. We created a network model that simulates parametrically the control of local gain by hypothetical top-down modification of local recurrence. These local gain changes, as a consequence of network dynamics in our model, enable modulation of contextual interactions in a task-dependent manner. Our model displays contour-related facilitation of neuronal responses and differential foreground vs. background responses over the neuronal ensemble, accounting for the perceptual pop-out of salient contours. It quantitatively reproduces the results of single-unit recording experiments in V1, highlighting salient contours and replicating the time course of contextual influences. We show by means of phase-plane analysis that the model operates stably even in the presence of large inputs. Our model shows how a simple form of top-down modulation of the effective connectivity of intrinsic cortical connections among biophysically realistic neurons can account for some of the response changes seen in perceptual learning and task switching.

Death Receptor 6 Regulates Adult Experience-Dependent Cortical Plasticity

Sensory experience alters cortical circuitry by parallel processes of axon outgrowth and pruning, but the mechanisms that control these rearrangements are poorly understood. Using in vivo 2-photon longitudinal imaging, we found a marked reduction in axonal pruning in somatosensory cortex of mice with a knock-out of the DR6 gene, which codes for Death Receptor 6. This effect was seen for both long-range horizontal excitatory connections and for the axons of inhibitory neurons. These results identify a new pathway governing axonal plasticity associated with experience-dependent changes in cortical maps.

Perception of illusory contours forms intermodulation responses of steady state visual evoked potentials as a neural signature of spatial integration

Perception of illusory contours was shown to be a consequence of neural activity related to spatial integration in early visual areas. Candidates for such filling-in phenomena are long-range horizontal connections of neurons in V1/V2, and feedback from higher order visual areas. To get a direct measure of spatial integration in early visual cortex, we presented two differently flickering inducers, which evoked steady-state visual evoked potentials (SSVEPs) while manipulating the formation of an illusory rectangle. As a neural marker of integration we tested differences in amplitudes of intermodulation frequencies i.e. linear combinations of the driving frequencies. These were significantly increased when an illusory rectangle was perceived. Increases were neither due to changes of any of the two driving frequencies nor in the frequency that tagged the processing of the compound object, indicating that results are not a consequence of paying more attention to inducers when the illusory rectangle was visible.

The role of feedback and long-range horizontal connections in brightness-related responses in visual cortex: a computational model

The role of feedback and long-range horizontal connections in brightness-related responses in visual cortex: a computational model

Multiplicative Mechanism of Lateral Interactions Revealed by Controlling Interhemispheric Input

Long-range horizontal connections are thought to modulate the responsiveness of neurons by supplying contextual information. A special type of long-range connections are interhemispheric projections, linking the 2 cerebral hemispheres. To investigate the action of those projections in a causal approach, we recorded in cat primary visual cortex while deactivating corresponding regions on the contralateral hemisphere. Interestingly, the action of callosal projections turned out to depend on the local and global composition of the stimulus: Full-field stimulation with gratings revealed moderate rate decreases (modulation index -0.24) and some significant increases (+0.21), whereas with lesser salient random dot textures, much more neurons were affected and reacted with pronounced rate decreases (-0.4). However, orientation and direction selectivity of those neurons were only slightly influenced by callosal input. This invariance could be achieved by scaling responses multiplicatively. Indeed, we could quantify the action of callosal input as a multiplicative scaling of responses, but additive scaling also occurred, especially for grating stimulation. We conclude that the quantitative action of long-range horizontal connections is by no means fixed but depends on how the network is driven by an external stimulus. Qualitatively, those connections seem to adjust the response gain of neurons, thereby preserving their selectivity.

Mathematical study of the formation of long-range horizontal connections and orientation maps in the primary visual cortex

Mathematical study of the formation of long-range horizontal connections and orientation maps in the primary visual cortex